Algebraic graph-assisted bidirectional transformers for molecular property prediction

• Published in: Nature communications Vol. 12; no. 1; pp. 3521 - 9
• Main Authors: Chen, Dong, Gao, Kaifu, Nguyen, Duc Duy, Chen, Xin, Jiang, Yi, Wei, Guo-Wei, Pan, Feng
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.06.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/92/606; 631/92/96; Algebra; Algorithms; Artificial neural networks; Blood-Brain Barrier - drug effects; Coders; Computer Simulation; Databases, Chemical; Datasets; Decision trees; Drug Discovery - methods; Drug-Related Side Effects and Adverse Reactions; Environmental protection; Graphical representations; Humanities and Social Sciences; Hydrophobic and Hydrophilic Interactions; Learning algorithms; Machine Learning; Molecular Conformation; Molecular properties; multidisciplinary; Neural networks; Neural Networks, Computer; Pharmaceutical Preparations - chemistry; Physical chemistry; Predictions; Science; Science (multidisciplinary); Toxicity; Transformers

The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although some machine learning models, such as bidirectional...